Neural net prediction of seismic streamer shape

ABSTRACT

A neural network to predict seismic streamer shape during seismic operations having an input layer, an optional hidden layer, and an output layer, each layer having one or more nodes. The first layer comprises input nodes attached to seismic data acquisition operational parameters as follows: vessel coordinates, receiver coordinates, time, vessel velocity, current velocity, wind velocity, water temperature, salinity, tidal information, water depth, streamer density, and streamer dimensions. Each node in the input layer is connected to each node in the hidden layer and each node in the hidden layer is connected to each node in the output layer, the output layer outputting a predicted cable shape. The layer maybe omitted. When the hidden lay is omitted, each node in the input layer is attached to each node in the output layer.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a continuation of the U.S. patent application Ser.No. 09/658,846, filed on Sep. 11, 2000, now U.S. Pat. No. 6,418,378.This application is a continuation in part of U.S. patent applicationSer. No. 09/603,068, filed on Jun. 26, 2000, now U.S. Pat. No.6,629,037, entitled “Optimal Paths for Marine Data Collection” which isincorporated herein by reference.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention relates to a system and method for the generationof a predicted cable shape during seismic data acquisition. Inparticular the invention provides a neural network trained to predictthe shape of a seismic streamer or receiver cable during sea borne,vessel-towed, seismic data collection operations.

2. Description of the Related Art

Cable shape and motion associated with sea borne towing is an importantfactor in determining the optimal path of a seismic vessel and itsassociated streamer of receivers during seismic data acquisitionoperations. In seismic data acquisition surveys, much of the subsurfaceterrain is improperly sampled or completely missed due to cablefeathering or displacement. Accurate prediction of the receiver cableshape is important to anticipate and compensate for the feathering ordisplacement of the seismic cable during seismic data acquisition. Themore accurately a survey path can be selected and executed, the moreoptimal and efficient the survey path becomes.

There are an infinite number of possible paths that the seismic towingvessel may traverse during the initial and secondary or in fill portionsof a seismic survey. Moreover, in many cases, the optimal traversal pathcan be difficult to determine. If optimal initial and in fill paths canbe identified, however, it significantly lowers the total effort andexpense associated with seismic data collection. Thus, there is a needfor an efficient means of determining the cable shape to attain optimalpaths in seismic surveying.

Targets missed on an initial pass have to be re-shot on secondarypasses. Each additional pass increases the cost of the survey. Suchsecondary passes significantly increase the time associated cost tocomplete a survey. Typical operating costs of a seismic vessel exceed$50,000 per day. Thus, predicting cable shape to attain an optimal pathwould result in an enormous cost savings for surveying each seismicprospect. These large cost reductions would provide a competitiveadvantage in the marine data collection market. Thus, cable shapeprediction is important in sampling the survey target area duringinitial and secondary passes. There is a long-felt need in the art forpredicting the shape of the seismic streamer during seismic dataacquisition operations.

SUMMARY OF THE INVENTION

The above-mentioned long-felt need has been met in accordance with thepresent invention with a neural network to predict seismic streamershape during seismic operations. In accordance with a preferredembodiment of the present invention, a system for predicting cable shapeis provided comprising a neural network having an input layer, anoptional hidden layer, and an output layer, each layer having one ormore nodes. The first layer comprises input nodes attached to seismicdata acquisition operational parameters as follows: vessel coordinates,receiver coordinates, time, vessel velocity, current velocity, windvelocity, water temperature, salinity, tidal information, water depth,streamer density, and streamer dimensions. Each node in the input layeris connected to each node in the hidden layer and each node in thehidden layer is connected to each node in the output layer, the outputlayer outputting a predicted cable shape. The hidden layer may beomitted. When the hidden lay is omitted, each node in the input layer isattached to each node in the output layer.

Each connection between nodes has an associated weight and a trainingprocess for determining the weights for each of the connections of theneural network. The trained neural network is responsive to the inputsand outputs to generate a predicted cable shape. The training processapplies a plurality of training sets to the neural network. Eachtraining set comprises a set of inputs and a desired cable shape. Witheach training data set, the training process determines the differencebetween the cable shape predicted by the neural network and the desiredor known cable shape. The training process then adjusts the weights ofthe neural network nodes based on the difference between the outputpredicted cable shape and the desired cable shape. The error assigned toeach node in the neural network may be assigned by the training processvia the use of back propagation or some other learning technique.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is an illustration of a neural network in a preferred embodimentof the present invention;

FIG. 2 is an example of a neural network having an input layer, a hiddenlayer and an output layer;

FIG. 3 is a process step chart showing the preferred steps executed intraining a neural network of the present invention; and

FIG. 4 is an illustration of forward activation flow and backward errorflow in a neural network.

DETAILED DESCRIPTION OF THE INVENTION

Neural networks are well known in the art. The following terminologywill useful to the understanding of the neural network of the presentinvention. A “Node” is a computational element in a neural network.A“Weight” is an adjustable value associated with a connection betweenthe nodes in a network. The magnitude of the weight determines theintensity of the connection. Negative weights inhibit node firing whilepositive weights enable node firing. “Connections” are the pathwaysbetween nodes that connect the nodes into a network.

A “Learning Law” is a mathematical relationship that modifies all orsome of the weights in a node's local memory in response to inputsignals. The Learning Law equation enables the neural network to adaptexamples of what it should be doing and thereby learn. Learning laws forweight adjustment can be described as supervised learning orunsupervised learning. Supervised learning assumes that the desiredoutput of the node is known or can be determined from an overall errorthat is used to update the weights.

In unsupervised learning the desired output is not known. Inunsupervised learning the weights associated with a node are not changedin proportion to the output error associated with a particular node butinstead are changed in proportion to some type of global reinforcementsignal. An “Activation function” is a mathematical relationship thatdetermines a node's output signal as a function of the most recent inputsignals and weights. “Back propagation” is the supervised learningmethod in which an output error signal is fed back through the network,altering connection weights so as to minimize the error. An “Inputlayer” is the layer of nodes for providing input to a neural network. A“Hidden layer” is the layer of nodes which are not directly connected toa neural network's input or output. An “Output layer” a layer of nodesthat provide access to the neural network's results.

The present invention is a neural network system and method forgenerating a predicted cable shape. FIG. 1 shows a neural network 101,and preprocessing unit 107. The neural network 101 generates a predictedcable shape 109 from input data applied to its input layer. Theoperational inputs to the neural network comprise vessel coordinates110, receiver coordinates 111, time 112, vessel velocity 113, currentvelocity 114, wind velocity 115 water temperature 116, salinity 117,tidal information 118, water depth 119, streamer density 120, andstreamer dimensions 121. These operational inputs are sensed in realtime and input to the neural network during seismic data collection.Additional operational data can be sensed and utilized as input to theneural network. Data input to the neural network may be preprocessed bythe preprocessing means 107 as shown in FIG. 1. Preprocessing can beused to normalize or recluster the input data.

The neural network 101 operates in three basic modes: training,operation and retraining. The training steps are shown in FIG. 3. Duringtraining the neural network is trained by use of a training means thatpresents the neural network with sets of training data. The trainingdata sets comprises vessel coordinates 1110, receiver coordinates 1111,time 1112, vessel velocity 1113, current velocity 1114, wind velocity1115, water temperature 1116, salinity 1117, tidal information 1118,water depth 1119, streamer density 1120, and streamer dimensions 1121and a desired output (i.e., actual, known, or correct output). Trainingdata is collected during actual operations or generated by a model andstored for later training of the neural network. Additional operationaldata obtained by sensing other operational parameters can be generatedand utilized as input to the neural network. The neural networkgenerates a predicted cable position based on the training inputs. Thispredicted cable shape is then compared with the desired or known output.The difference between the predicted cable position generated by theneural network and the desired or known cable position is used to adjustthe weights of the nodes in the neural network through back propagationor some other learning technique.

During training the neural network learns and adapts to the inputspresented to it. After the neural network is trained it can be utilizedto make a cable position prediction for a given input data set. Thismode of operation is referred to as the operational mode. After theoperational mode the neural network can be retrained with additionaldata collected from other surveys. Thus, the neural network making acable position prediction for one survey, may be retrained with datafrom a second survey. The retrained neural network can then be used tomake a prediction of cable position for the second survey.

Referring now to FIG. 2, a representative example of a neural network isshown. It should be noted that the example shown in FIG. 2 is merelyillustrative of one embodiment of a neural network. As discussed below,other embodiments of a neural network can be used. The embodiment ofFIG. 2 has an input layer 205, a hidden layer (or middle layer) 203 anda output layer 201. The input layer 205 includes a layer of input nodeswhich take their input values 207 from the external input (vesselcoordinates, receiver coordinates, time, vessel velocity, currentvelocity, wind velocity, water temperature, salinity, tidal information,water depth, streamer density, and streamer dimensions.). The input datais used by the neural network to generate the output 209 (or cableposition). Even though the input layer 205 is referred to as a layer ofthe neural network, the input layer 205 does not contain any processingnodes.

The middle layer is called the hidden layer 203. A hidden layer is notrequired but, is usually provided. The outputs from the nodes of theinput layer 205 are input to each node in the hidden layer 203. Likewisethe outputs of nodes of the hidden layer 203 are input to each node inthe output layer 201. Additional hidden layers can be used. Each node inadditional hidden layers take the outputs from the previous layer astheir input.

The output layer 201 may consist of one or more nodes. The output layerreceives the output of nodes of the hidden layer 203. The output(s) ofthe node(s) of the output layer 201 are the predicted cable shape 209.Each connection between nodes has an associated weight. Weightsdetermine the relative effect each input value has on each output value.Random values are initially selected for each of the weights. Theweights are modified as the network is trained.

The present invention contemplates other types of neural networkconfigurations for use with a neural network. All that is required for aneural network is that the neural network be able to be trained andretrained so as to provide the needed predicted cable position.

Input data 207 is provided to input computer memory storage locationsrepresenting input nodes in the input layer 205. The hidden layer 203nodes each receive input values from all of the inputs in the inputlayer 205. Each hidden layer node has a weight associated with eachinput value. Each node multiplies each input value times its associatedweight, and sums these values for all of the inputs. This sum is thenused as input to an equation (also called a transfer function oractivation function) to produce an output for that node. The processingfor nodes in the hidden layer 203 can be performed in parallel, or theycan be performed sequentially. In the neural network with only onehidden layer 203 as shown in FIG. 2, the output values or activationswould then be computed. Each output or activation is multiplied by itsassociated weight, and these values are summed. This sum is then used asinput to an equation which produces the predicted cable shape 209 as itsresult. Thus using input data 207, a neural network produces an output209 which is as a predicted value. An equivalent function can beachieved using analog apparatus.

The output of a node is a function of the weighted sum of its inputs.The input/output relationship of a node is often described as thetransfer function. The activation function can be representedsymbolically as follows:

Y=f(Σ(w _(i) x _(i)))

It is the weighted sum, Σ(w_(i)x_(i)), that is input to the activationfunction. The activation function determines the activity levelgenerated in the node as a result of an input signal. Any function maybe selected as the activation function. However, for use with backpropagation a sigmoidal function is preferred. The sigmoidal function iscontinuous S-shaped monotonically increasing function whichasymptotically approaches fixed values as the input approaches plus orminus infinity. Typically the upper limit of the sigmoid is set to +1and the lower limit is set to either 0 or −1. A sigmoidal function canbe represented as follows:

f(x)=1/(1+e ^(−(x+T)))

where x is weighted input (i.e., (w_(i) x_(i))) and T is a simplethreshold or bias.

Note that the threshold T in the above equation can be eliminated byincluding a bias node in the neural network. The bias node has no inputbut outputs a constant value to all output and hidden layer nodes in theneural network. The weights that each node assigns to this one outputbecome the threshold term for the given node. This simplifies theequation to f(x)=1/(1+e^(−x)) where x is weighted input (i.e., (w₁ x₁).where X₀=1, and W₀ is added as a weight).

A relational or object oriented database is suitable for use with thepresent invention. There are many commercial available databasessuitable for use with the present invention.

The adjustment of weights in a neural network is commonly referred to astraining. Training a neural network requires that training data beassembled for use by the training procedure. The training procedure thenimplements the steps shown in FIG. 3 and described below. Referring nowto FIG. 3, the present invention contemplates various approaches fortraining the neural network. In step 300 the weights are initialized torandom values. When retraining the neural network step 300 may beskipped so that training begins with the weights computed from previoustraining session(s). In step 301 a set of input data is applied to theinputs of the neural network. This input data causes the nodes in theinput layer to generate outputs to the nodes of the hidden layer, whichin turn generate outputs to nodes of the output layer which produce aresult. This flow of information from the input nodes to the outputnodes is typically referred to as forward activation flow as shown onthe right side of FIG. 4.

Returning now to FIG. 3, associated with the input data applied to theneural network in step 301 is a desired, actual or known output value.In step 303 the predicted cable shape produced by the neural network iscompared with the desired, actual or known output. The differencebetween the desired output and the predicted cable shape produced by theneural network is referred to as the error value. This error value isthen used to adjust the weights in the neural network as depicted instep 305.

One suitable approach for adjusting weights is called back propagationin which the output error signal is fed back through the network,altering connection weights so as to minimize that error. Backpropagation distributes the overall error value to each of the nodes inthe neural network, adjusting the weights associated with each node'sinputs based on the error value allocated to it. This backward errorflow is depicted on the left hand side of FIG. 4.

Once the error associated with a given node is known, the node's weightsis adjusted. One way of adjusting the weight for a given node is asfollows:

Wnew=Wold+βEX

where E is the error signal associated with the node, X represents theinputs, Wold represents the current weights, Wnew represents the weightsafter adjustment, and β is a learning constant or the size of the stepstaken down the error curve. Other variations of this method can be usedwith the present invention. For example the following

Wnew=Wold+βEX+α.(Wnew−Wold)prev

includes a momentum term, α(Wnew−Wold)prev, where α is a constant thatis multiplied by the change in the weight from a previous input pattern.

The back propagation or other learning technique is repeated with eachof the training sets until training is complete. As shown in step 307 avalidation test is used to determine whether training is complete. Thisvalidation test could simply check that the error value is less than acertain value for a given number of iterations or simply end trainingafter a certain number of iterations. A preferred technique is to use aset of testing data and measure the error generated by the testing data.The testing data could be generated so that it is mutually exclusive ofthe data used for training. If the error resulting from application ofthe testing data is less than a predetermined value, training isconsidered complete. The weights are not adjusted as a result ofapplying the validation testing data to the neural network.

Note that although the present invention has been described with respectto the basic back propagation algorithm other variations of the backpropagation algorithm may be used with the present invention as well.Other learning laws may also be used. For instance, reinforcementlearning. In reinforcement learning a global reinforcement signal isapplied to all nodes in the neural network. The nodes then adjust theirweights based on the reinforcement signal. This is decidedly differentfrom back propagation techniques which essentially form an error signalat the output of each node in the network. In reinforcement learningthere is only one error signal which is used by all nodes.

The training sets are then used to adjust the weights in the neuralnetwork as described above. Any given training set may be utilizedmultiple times in a training session. After the neural network istrained operational data is applied to the trained neural network togenerate the predicted cable shape.

A preprocessing function 107 is depicted in FIG. 1. Preprocessing of theinput values may be performed as the inputs are being applied to theneural network. Back propagation has been found to work best when theinput data is normalized either in the range [−1,1] or [0,1]. Note thatnormalization is performed for each factor of data. The normalizationstep may also be combined with other steps such as taking the naturallog of the input. The logarithmic scale compacts large data values morethan smaller values. When the neural network contains nodes with asigmoidal activation function, better results are achieved if the datais normalized over the range [0.2, 0.8]. Normalizing to range [0.2, 0.8]uses the heart of the sigmoidal activation function. Other functions maybe utilized to preprocess the input value.

The preferred embodiment of the present invention comprises one or moresoftware systems. In this context, a software system is a collection ofone or more executable software programs, and one or more storage areas,for example, RAM or disk. In general terms, a software system should beunderstood to comprise a fully functional software embodiment of afunction, which can be added to an existing computer system to providenew function to that computer system.

Software systems generally are constructed in a layered fashion. In alayered system, a lowest level software system is usually the computeroperating system that enables the hardware to execute softwareinstructions. Additional layers of software systems may provide, forexample, database capability. This database system provides a foundationlayer on which additional software systems can be built. For example, aneural network software system can be layered on top of the database.

A software system is thus understood to be a software implementation ofa function that can be assembled in a layered fashion to produce acomputer system providing new functionality. Also, in general, theinterface provided by one software system to another software system iswell-defined. It should be understood in the context of the presentinvention that delineations between software systems are representativeof the preferred implementation. However, the present invention may beimplemented using any combination or separation of software systems.

The database can be implemented as a stand-alone software system whichforms a foundation layer on which other software systems, (e.g., such asthe neural network, and training means) can be layered. The database, asused in the present invention, can be implemented using a number ofmethods. For example, the database can be built as a random accessmemory (RAM) database, a disk-based database, or as a combination of RAMand disk databases. The present invention contemplates any computer oranalog means of performing the functions of the database. These includethe use of flat files, relational data bases, object oriented databasesor hierarchical data bases to name a few.

The neural network retrieves input data and uses this retrieved inputdata to output a predicted cable shape. The output data can be suppliedto the database for storage or can be sent to other software systemssuch as decision making or planning applications. The input data can beobtained from the database.

It should also be understood with regard to the present invention thatsoftware and computer embodiments are only one possible way ofimplementing the various elements in the systems and methods. Asmentioned above, the neural network may be implemented in analog ordigital form. It should be understood, with respect to the method stepsas described above for the functioning of the systems as described inthis section, that operations such as computing or determining (whichimply the operation of a digital computer), may also be carried out inanalog equivalents or by other methods.

The neural network model can have a fully connected aspect, or a nofeedback aspect. These are just examples. Other aspects or architecturesfor the neural network model are contemplated.

The neural network must have access to input data and training data andaccess to locations in which it can store output data and error data.One embodiment of the present invention uses an approach where the datais not kept in the neural network. Instead, data pointers are kept inthe neural network which point to data storage locations (e.g., aworking memory area) in a separate software system. These data pointers,also called data specifications, can take a number of forms and can beused to point to data used for a number of purposes. For example, inputdata pointer and output data pointer must be specified. The pointer canpoint to or use a particular data source system for the data, a datatype, and a data item pointer. Neural network must also have a dataretrieval function and a data storage function. Examples of thesefunctions are callable routines, disk access, and network access. Theseare merely examples of the aspects of retrieval and storage functions.The preferred method is to have the neural network utilize data in thedatabase. The neural network itself can retrieve data from the databaseor another module could feed data to the areas specified by the neuralnetworks pointers.

The neural network also needs to be trained, as discussed above. Asstated previously, any presently available or future developed trainingmethod is contemplated by the present invention. The training methodalso may be somewhat dictated by the architecture of the neural networkmodel that is used. Examples of aspects of training methods include backpropagation, generalized delta, and gradient descent, all of which arewell known in the art.

There are several aids for the development of neural networks commonlyavailable. For example, the IBM Neural Network Utility (NNU) providesaccess to a number of neural paradigms (including back propagation)using a graphical user interface (GUI) as well as an applicationprogrammer's interface (API) which allows the network to be embedded ina larger system. The NNU GUI runs on Intel-based machines using OS/2 orDOS/Windows and on RISC/6000 machines using AIX. The API is availablenot only on those platforms but also on a number of mainframe platforms,including VM/CMS and OS/400. Available hardware for improving neuralnetwork training and run-time performance includes the IBM Wizard, acard that plugs into MicroChannel buses. Other vendors with similarsoftware and/or hardware products include NeuralWare, Nestor andHecht-Nielsen Co.

The set of inputs to the neural network can be preprocessed. Thepreferable technique for normalizing the inputs is to take the naturallog of the input and then normalize it to a value between 0.2 and 0.8.In this way, it can be assured that the “heart” of the sigmoidalfunction would be utilized. This ameliorated the problems implicit invalues that lie on the edges of the function, near 0 and 1. If the datawas simply normalized between 0.2 and 0.8, the percentage error wouldtend to be much larger in the smaller districts. The error, on average,is approximately equal for all inputs; however, an equal error on asmaller district will cause a larger percentage error than in a largerdistrict. To minimize this effect, the data is normalized. The naturallog of the data is taken first, which collapses the data and produces amore normal distribution. We then normalize these natural logs andpresent them to the network.

A feed forward network using twelve inputs nodes, a hidden layer, oneoutput node and standard back propagation performs the cable prediction.Inputs nodes using different operational data and more or less hiddenmay also be used.

While the present invention has been described using a cable predictiontechnique and return volume applications as examples, the presentinvention is not limited to these particular applications.

While the invention has been described in detail herein in accord withcertain preferred embodiments thereof, modifications and changes thereinmay be effected by those skilled in the art. Accordingly, it is intendedby the appended claims to cover all such modifications and changes asfall within the true spirit and scope of the invention.

What is claimed is:
 1. A cable shape prediction system comprising: aneural network comprising an input layer, a hidden layer, and an outputlayer, each layer comprising one or more nodes, nodes in the input layerbeing connected to operational data, at least one node in the inputlayer being connected to at least one node in the hidden layer and atleast one node in the hidden layer being connected to at least one nodein the output layer, the output layer outputting a predicted cableposition, each connection between nodes having an associated weight; anda training apparatus for determining the weight for each said connectionbetween nodes of the neural network, the neural network being responsiveto the operational inputs for outputting a predicted cable position. 2.The system of claim 1 wherein the training apparatus comprises:apparatus for applying a plurality of training sets to the neuralnetwork, each training set consisting of historical data and a desiredforecast cable position; apparatus for determining for each set oftraining data a difference between the forecast produced by the neuralnetwork and the desired forecast cable position; and apparatus foradjusting each weight of the neural network based on the difference. 3.The system in claim 2 wherein the training apparatus comprises apparatusfor adjusting each weight by use of back propagation.
 4. The system inclaim 3 wherein the training apparatus further comprises means forapplying a test data set to the neural network to determine whethertraining is complete.
 5. The system in claim 4 wherein the test data setis a validation data set.
 6. The system in claim 1 further comprising apreprocessor for computing a logarithmic value for each historical datumand for connecting each logarithmic value to the input layer.
 7. Thesystem in claim 1 wherein the neural network includes a bias node thathas connections to at least one node in the hidden layer and at leastone node in the output layer.
 8. The system of claim 1 furthercomprising a normalizing apparatus for normalizing inputs to the neuralnetwork.
 9. The system of claim 1 further comprising: at least one inputfor receiving at least one of vessel coordinates, receiver coordinates,time, vessel velocity, current velocity, wind velocity, watertemperature, salinity, tidal information, water depth, streamer densityand streamer dimensions as input to the neural network; and at least oneoutput for generating a predicted cable shape output.
 10. The system ofclaim 1 wherein the learning apparatus uses reinforcement learning. 11.A method for cable shape prediction comprising: providing operationaldata to a neural network input layer; and outputting a predicted cableposition.
 12. The method of claim 11 further comprising: applying aplurality of training sets to the neural network, each training setconsisting of historical data, an associated statistical forecast cableshape made by the neural net and a desired forecast cable shape;determining for each set of training data a difference between theforecast produced by the neural network and the desired forecast cableshape; and adjusting each weight of the neural network based on thedifference.
 13. The method of claim 12 further comprising: adjustingeach weight is performed by use of back propagation.
 14. The method ofclaim 12 further comprising: applying a test data set to the neuralnetwork.
 15. The method of claim 12 further comprising: applying averification data set to the neural network.
 16. The method of claim 11further comprising: computing a logarithmic value for each historicaldatum and for connecting each logarithmic value to the input layer. 17.The method of claim 11 further comprising: applying a bias signal tonodes in the hidden layer and to nodes in the output layer.
 18. Themethod of claim 11 further comprising: normalizing inputs to the neuralnetwork.
 19. The method of claim 11 further comprising: receivingoperational data as input to the neural network; and generating apredicted cable shape output.
 20. The method of claim 11 furthercomprising: using reinforcement learning to train the neural network.